1. YKL REA Northern Pike Model Photo: ADF&G

2. Fish Distribution Models Photo: USFWS Evaluate model performance Classification tree and random forest models ADF&G AFFID species occurrence data GIS source data Predict species habitat across REA study area Fish distributions Create stream network and landscape predictor variables in GIS Process AFFID data for use in models

3. Stream Network Used TauDEM to process DEM 1.Add in additional HUCs on boundary of study area that flow into the study area 2.Fill pits 3.Calculate flow direction (D8 method) 4.Calculate contributing area 5.Create stream network based on curvature method and drop analysis

4. Predictor Variables Photo: USFWS Predictors of Fish Habitat Elevation Permafrost Gradient Floodplain Slope over area ratio Stream order Watershed area Average watershed annual precipitation Average watershed annual temperature Average watershed elevation Average watershed slope over area ratio Average watershed slope Percent permafrost cover in watershed Percent lake cover in watershed

5. Process AFFID data -Presences from AFFID and ADF&G/BLM telemetry project in Kuskokwim -Absences from projects in AFFID that listed fish community sampling as an objective -Resampled data in areas of high intensity (Pebble area and telemetry) -Shifted points along flow direction grid until they reached the stream network -Extracted all predictor variables to each data point

6. Classification Trees Photo: USFWS Classification Tree Analysis Steps: – Identify the groups – Choose the variables – Identify the split that maximizes the homogeneity of the resulting groups – Determine a stopping point for the tree – Prune the tree using cross-validation Absent 0.97 (263) Asterospicularia laurae Shelf: Inner, Mid Shelf: Outer Absent 0.78 (64) Location: Back, Flank Location: Front Depth < 3m Depth ≥ 3m (De'Ath and Fabricious 2000) Absent 0.56 (9) Present 0.81 (37) Misclassification rates: Null = 15%, Model = 9%

7. Random Forests Creates many classification trees and combines predictions from all of them: -Start with bootstrapped samples of data -Observations not included are called out-of-bag (OOB) -Fit a classification tree to each bootstrap sample, for each node, use a subset of the predictor variables -Determine the predicted class for each observation based on majority vote of OOB predictions -To determine variable importance, compare misclassification rates for OOB observations using true and randomly permuted data for each predictor

8. Run models in R ct1<-mvpart(pres.f~.,data=fish.pred1[s1,],xv="1se") rf1<-randomForest(pres.f~.,data=fish.pred1[s1,],ntree=999) Photo: USFWS CT trainingCT validationRF trainingRF validation 10.0960.1610.1080.113 20.1080.1940.0920.161 30.120.1610.0960.097 40.120.1450.1160.129 50.1080.1940.1080.145 60.0720.0970.1120.048 70.1240.1770.1080.097 80.1120.0970.1040.081 90.1370.0810.1240.065 100.120.1450.1410.097 summary0.11170.14520.11090.1033

9. Model Performance Photo: USFWS Confusion Matrix 01Error 0184136.6% 1219318.4% Top five variables are watershed area, stream order, stream elevation, percent of watershed covered by lakes, and stream floodplain.

10. Northern Pike Results: ~ 10,900 km of predicted summer habitat (restricted to stream reaches > 1 km in length) PredictorPresenceAbsence Watershed area13,000 km 2 60 km 2 Stream elevation 60 m200 m Stream floodplainYesNo Watershed lake cover 2.8%2.1% Stream order4 th 1 st

11. Invasive Macrophytes Climate Change Precipitation Permafrost Fire Human Uses Mining Infrastructure Harvest Contaminants Temperature Permafrost thaw Reduction in age at maturity and shift in spawning season Bioaccumulation of mercury in adults Expanded ice-free season Temporary increases in nutrient inputs Elodea ssp could reduce quality of spawning habitat In creased toxicity Increased potential for establishment of invasive macrophytes and changing fire dynamics Increased contaminant sources Change in deposition rates Northern Pike Esox lucius Habitat Increase depth of active layer will increase lake drainage area Subsistence harvest pressures on overwintering populations Direct destruction of habitat, hindrance of migration routes, increased downstream turbidity and sedimentation Change Agents Drivers CE General Effect CE-Specific Effect Increased winter precipitation may increase overwintering habitat

12. Review Please review and provide comments: -Distribution models for fish and habitats -Conceptual models and text descriptions for fish Contact: Rebecca Shaftel rsshaftel@uaa.alaska.edursshaftel@uaa.alaska.edu, 907-786-4965 Photo: USFWS